Multinational prediction of household and personal exposure to fine particulate matter (PM2.5) in the PURE cohort study

Abstract

Introduction: Use of polluting cooking fuels generates household air pollution (HAP) containing health-damaging levels of fine particulate matter (PM_2.5). Many global epidemiological studies rely on categorical HAP exposure indicators, which are poor surrogates of measured PM_2.5 levels. To quantitatively characterize HAP levels on a large scale, a multinational measurement campaign was leveraged to develop household and personal PM_2.5 exposure models.

Methods: The Prospective Urban and Rural Epidemiology (PURE)-AIR study included 48-hour monitoring of PM_2.5 kitchen concentrations (n = 2,365) and male and/or female PM_2.5 exposure monitoring (n = 910) in a subset of households in Bangladesh, Chile, China, Colombia, India, Pakistan, Tanzania and Zimbabwe. PURE-AIR measurements were combined with survey data on cooking environment characteristics in hierarchical Bayesian log-linear regression models. Model performance was evaluated using leave-one-out cross validation. Predictive models were applied to survey data from the larger PURE cohort (22,480 households; 33,554 individuals) to quantitatively estimate PM_2.5 exposures.

Results: The final models explained half (R² = 54%) of the variation in kitchen PM_2.5 measurements (root mean square error (RMSE) (log scale):2.22) and personal measurements (R² = 48%; RMSE (log scale):2.08). Primary cooking fuel type, heating fuel type, country and season were highly predictive of PM_2.5 kitchen concentrations. Average national PM_2.5 kitchen concentrations varied nearly 3-fold among households primarily cooking with gas (20 μg/m³ (Chile); 55 μg/m³ (China)) and 12-fold among households primarily cooking with wood (36 μg/m³ (Chile)); 427 μg/m³ (Pakistan)). Average PM_2.5 kitchen concentration, heating fuel type, season and secondhand smoke exposure were significant predictors of personal exposures. Modeled average PM_2.5 female exposures were lower than male exposures in upper-middle/high-income countries (India, China, Colombia, Chile).

Conclusion: Using survey data to estimate PM_2.5 exposures on a multinational scale can cost-effectively scale up quantitative HAP measurements for disease burden assessments. The modeled PM_2.5 exposures can be used in future epidemiological studies and inform policies targeting HAP reduction.

Keywords: Bayesian hierarchical modeling; Household air pollution; Kitchen concentrations; PM(2.5); Personal exposures; Predictive modeling.